Matching Sparse Sets of Cardiac Image Cross-Sections Using Large Deformation Diffeomorphic Metric Mapping Algorithm

  • Siamak Ardekani
  • Aastha Jain
  • Saurabh Jain
  • Theodore P. Abraham
  • Maria R. Abraham
  • Stefan Zimmerman
  • Raimond L. Winslow
  • Michael I. Miller
  • Laurent Younes
Conference paper

DOI: 10.1007/978-3-642-28326-0_24

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7085)
Cite this paper as:
Ardekani S. et al. (2012) Matching Sparse Sets of Cardiac Image Cross-Sections Using Large Deformation Diffeomorphic Metric Mapping Algorithm. In: Camara O., Konukoglu E., Pop M., Rhode K., Sermesant M., Young A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2011. Lecture Notes in Computer Science, vol 7085. Springer, Berlin, Heidelberg

Abstract

The purpose of this study is to illustrate the application of large deformation diffeomorphic metric mapping to perform registration among sparsely sampled cardiac magnetic resonance imaging (MRI) data. To evaluate the performance of this method, we use two sets of data: 1) contours that are generated from sparsely sampled left ventricular sections and extracted from short axis cardiac MRI of patients with hypertrophic cardiomyopathy and 2) left ventricular surface mesh that is generated from higher resolution cardiac computed tomography image. We present two different discrepancy criteria, one based on a measure that is embedded in the dual of a reproducing kernel Hilbert space of functions for curves and the other is based on a geometric soft matching distance between a surface and a curve.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Siamak Ardekani
    • 1
  • Aastha Jain
    • 1
  • Saurabh Jain
    • 1
  • Theodore P. Abraham
    • 2
  • Maria R. Abraham
    • 2
  • Stefan Zimmerman
    • 2
  • Raimond L. Winslow
    • 3
  • Michael I. Miller
    • 1
    • 3
  • Laurent Younes
    • 1
    • 3
  1. 1.Center for Imaging SciencesJohns Hopkins UniversityUSA
  2. 2.Medical InstitutionsJohns Hopkins UniversityUSA
  3. 3.Institute for Computational MedicineJohns Hopkins UniversityUSA

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